SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation. More...

#include <pcl/segmentation/sac_segmentation.h>

Inheritance diagram for pcl::SACSegmentationFromNormals< PointT, PointNT >:

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Collaboration diagram for pcl::SACSegmentationFromNormals< PointT, PointNT >:

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List of all members.

Public Types
typedef SACSegmentation < PointT >::PointCloud	PointCloud
typedef PointCloud::Ptr	PointCloudPtr
typedef PointCloud::ConstPtr	PointCloudConstPtr
typedef pcl::PointCloud< PointNT >	PointCloudN
typedef PointCloudN::Ptr	PointCloudNPtr
typedef PointCloudN::ConstPtr	PointCloudNConstPtr
typedef SampleConsensus < PointT >::Ptr	SampleConsensusPtr
typedef SampleConsensusModel < PointT >::Ptr	SampleConsensusModelPtr
typedef SampleConsensusModelFromNormals < PointT, PointNT >::Ptr	SampleConsensusModelFromNormalsPtr
typedef PointIndices::Ptr	PointIndicesPtr
typedef PointIndices::ConstPtr	PointIndicesConstPtr
Public Member Functions
	SACSegmentationFromNormals ()
	Empty constructor.
void	setInputNormals (const PointCloudNConstPtr &normals)
	Provide a pointer to the input dataset that contains the point normals of the XYZ dataset.
PointCloudNConstPtr	getInputNormals () const
	Get a pointer to the normals of the input XYZ point cloud dataset.
void	setNormalDistanceWeight (double distance_weight)
	Set the relative weight (between 0 and 1) to give to the angular distance (0 to pi/2) between point normals and the plane normal.
double	getNormalDistanceWeight () const
	Get the relative weight (between 0 and 1) to give to the angular distance (0 to pi/2) between point normals and the plane normal.
void	setDistanceFromOrigin (const double d)
	Set the distance we expect a plane model to be from the origin.
double	getDistanceFromOrigin () const
	Get the distance of a plane model from the origin.
void	setModelType (int model)
	The type of model to use (user given parameter).
int	getModelType () const
	Get the type of SAC model used.
SampleConsensusPtr	getMethod () const
	Get a pointer to the SAC method used.
SampleConsensusModelPtr	getModel () const
	Get a pointer to the SAC model used.
void	setMethodType (int method)
	The type of sample consensus method to use (user given parameter).
int	getMethodType () const
	Get the type of sample consensus method used.
void	setDistanceThreshold (double threshold)
	Distance to the model threshold (user given parameter).
double	getDistanceThreshold () const
	Get the distance to the model threshold.
void	setMaxIterations (int max_iterations)
	Set the maximum number of iterations before giving up.
int	getMaxIterations () const
	Get maximum number of iterations before giving up.
void	setProbability (double probability)
	Set the probability of choosing at least one sample free from outliers.
double	getProbability () const
	Get the probability of choosing at least one sample free from outliers.
void	setOptimizeCoefficients (bool optimize)
	Set to true if a coefficient refinement is required.
bool	getOptimizeCoefficients () const
	Get the coefficient refinement internal flag.
void	setRadiusLimits (const double &min_radius, const double &max_radius)
	Set the minimum and maximum allowable radius limits for the model (applicable to models that estimate a radius)
void	getRadiusLimits (double &min_radius, double &max_radius)
	Get the minimum and maximum allowable radius limits for the model as set by the user.
void	setAxis (const Eigen::Vector3f &ax)
	Set the axis along which we need to search for a model perpendicular to.
Eigen::Vector3f	getAxis () const
	Get the axis along which we need to search for a model perpendicular to.
void	setEpsAngle (double ea)
	Set the angle epsilon (delta) threshold.
double	getEpsAngle () const
	Get the epsilon (delta) model angle threshold in radians.
virtual void	segment (PointIndices &inliers, ModelCoefficients &model_coefficients)
	Base method for segmentation of a model in a PointCloud given by <setInputCloud (), setIndices ()>
virtual void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset.
PointCloudConstPtr const	getInputCloud ()
	Get a pointer to the input point cloud dataset.
void	setIndices (const IndicesPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
void	setIndices (const IndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
void	setIndices (const PointIndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
void	setIndices (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols)
	Set the indices for the points laying within an interest region of the point cloud.
IndicesPtr const	getIndices ()
	Get a pointer to the vector of indices used.
const PointT &	operator[] (size_t pos)
	Override PointCloud operator[] to shorten code.

Detailed Description

template<typename PointT, typename PointNT>
class pcl::SACSegmentationFromNormals< PointT, PointNT >

SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation.

Definition at line 265 of file sac_segmentation.h.

Member Typedef Documentation

template<typename PointT , typename PointNT >

typedef SACSegmentation<PointT>::PointCloud pcl::SACSegmentationFromNormals< PointT, PointNT >::PointCloud

Reimplemented from pcl::SACSegmentation< PointT >.

Definition at line 278 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

typedef PointCloud::ConstPtr pcl::SACSegmentationFromNormals< PointT, PointNT >::PointCloudConstPtr

Reimplemented from pcl::SACSegmentation< PointT >.

Definition at line 280 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

typedef pcl::PointCloud<PointNT> pcl::SACSegmentationFromNormals< PointT, PointNT >::PointCloudN

Definition at line 282 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

typedef PointCloudN::ConstPtr pcl::SACSegmentationFromNormals< PointT, PointNT >::PointCloudNConstPtr

Definition at line 284 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

typedef PointCloudN::Ptr pcl::SACSegmentationFromNormals< PointT, PointNT >::PointCloudNPtr

Definition at line 283 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

typedef PointCloud::Ptr pcl::SACSegmentationFromNormals< PointT, PointNT >::PointCloudPtr

Reimplemented from pcl::SACSegmentation< PointT >.

Definition at line 279 of file sac_segmentation.h.

template<typename PointT>

typedef PointIndices::ConstPtr pcl::PCLBase< PointT >::PointIndicesConstPtr [inherited]

Reimplemented in pcl::EuclideanClusterExtraction< PointT >, pcl::ExtractPolygonalPrismData< PointT >, pcl::SegmentDifferences< PointT >, pcl::LabeledEuclideanClusterExtraction< PointT >, pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >, and pcl::PCA< PointT >.

Definition at line 79 of file pcl_base.h.

template<typename PointT>

typedef PointIndices::Ptr pcl::PCLBase< PointT >::PointIndicesPtr [inherited]

Reimplemented in pcl::EuclideanClusterExtraction< PointT >, pcl::ExtractPolygonalPrismData< PointT >, pcl::SegmentDifferences< PointT >, pcl::LabeledEuclideanClusterExtraction< PointT >, pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >, and pcl::PCA< PointT >.

Definition at line 78 of file pcl_base.h.

template<typename PointT , typename PointNT >

typedef SampleConsensusModelFromNormals<PointT, PointNT>::Ptr pcl::SACSegmentationFromNormals< PointT, PointNT >::SampleConsensusModelFromNormalsPtr

Definition at line 288 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

typedef SampleConsensusModel<PointT>::Ptr pcl::SACSegmentationFromNormals< PointT, PointNT >::SampleConsensusModelPtr

Reimplemented from pcl::SACSegmentation< PointT >.

Definition at line 287 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

typedef SampleConsensus<PointT>::Ptr pcl::SACSegmentationFromNormals< PointT, PointNT >::SampleConsensusPtr

Reimplemented from pcl::SACSegmentation< PointT >.

Definition at line 286 of file sac_segmentation.h.

Constructor & Destructor Documentation

template<typename PointT , typename PointNT >

pcl::SACSegmentationFromNormals< PointT, PointNT >::SACSegmentationFromNormals ( ) [inline]

Empty constructor.

Definition at line 291 of file sac_segmentation.h.

Member Function Documentation

template<typename PointT>

Eigen::Vector3f pcl::SACSegmentation< PointT >::getAxis ( ) const [inline, inherited]

Get the axis along which we need to search for a model perpendicular to.

Definition at line 190 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

double pcl::SACSegmentationFromNormals< PointT, PointNT >::getDistanceFromOrigin ( ) const [inline]

Get the distance of a plane model from the origin.

Definition at line 324 of file sac_segmentation.h.

template<typename PointT>

double pcl::SACSegmentation< PointT >::getDistanceThreshold ( ) const [inline, inherited]

Get the distance to the model threshold.

Definition at line 127 of file sac_segmentation.h.

template<typename PointT>

double pcl::SACSegmentation< PointT >::getEpsAngle ( ) const [inline, inherited]

Get the epsilon (delta) model angle threshold in radians.

Definition at line 200 of file sac_segmentation.h.

template<typename PointT>

IndicesPtr const pcl::PCLBase< PointT >::getIndices ( ) [inline, inherited]

Get a pointer to the vector of indices used.

Definition at line 190 of file pcl_base.h.

template<typename PointT>

PointCloudConstPtr const pcl::PCLBase< PointT >::getInputCloud ( ) [inline, inherited]

Get a pointer to the input point cloud dataset.

Definition at line 107 of file pcl_base.h.

template<typename PointT , typename PointNT >

PointCloudNConstPtr pcl::SACSegmentationFromNormals< PointT, PointNT >::getInputNormals ( ) const [inline]

Get a pointer to the normals of the input XYZ point cloud dataset.

Definition at line 302 of file sac_segmentation.h.

template<typename PointT>

int pcl::SACSegmentation< PointT >::getMaxIterations ( ) const [inline, inherited]

Get maximum number of iterations before giving up.

Definition at line 137 of file sac_segmentation.h.

template<typename PointT>

SampleConsensusPtr pcl::SACSegmentation< PointT >::getMethod ( ) const [inline, inherited]

Get a pointer to the SAC method used.

Definition at line 103 of file sac_segmentation.h.

template<typename PointT>

int pcl::SACSegmentation< PointT >::getMethodType ( ) const [inline, inherited]

Get the type of sample consensus method used.

Definition at line 117 of file sac_segmentation.h.

template<typename PointT>

SampleConsensusModelPtr pcl::SACSegmentation< PointT >::getModel ( ) const [inline, inherited]

Get a pointer to the SAC model used.

Definition at line 107 of file sac_segmentation.h.

template<typename PointT>

int pcl::SACSegmentation< PointT >::getModelType ( ) const [inline, inherited]

Get the type of SAC model used.

Definition at line 99 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

double pcl::SACSegmentationFromNormals< PointT, PointNT >::getNormalDistanceWeight ( ) const [inline]

Get the relative weight (between 0 and 1) to give to the angular distance (0 to pi/2) between point normals and the plane normal.

Definition at line 314 of file sac_segmentation.h.

template<typename PointT>

bool pcl::SACSegmentation< PointT >::getOptimizeCoefficients ( ) const [inline, inherited]

Get the coefficient refinement internal flag.

Definition at line 157 of file sac_segmentation.h.

template<typename PointT>

double pcl::SACSegmentation< PointT >::getProbability ( ) const [inline, inherited]

Get the probability of choosing at least one sample free from outliers.

Definition at line 147 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::getRadiusLimits	(	double &	min_radius,
		double &	max_radius
	)		`[inline, inherited]`

Get the minimum and maximum allowable radius limits for the model as set by the user.

Parameters:

[out]	min_radius	the resultant minimum radius model
[out]	max_radius	the resultant maximum radius model

Definition at line 176 of file sac_segmentation.h.

template<typename PointT>

const PointT& pcl::PCLBase< PointT >::operator[] ( size_t pos ) [inline, inherited]

Override PointCloud operator[] to shorten code.

Note:: this method can be called instead of (*input_)[(*indices_)[pos]] or input_->points[(*indices_)[pos]]

Parameters:

pos	position in indices_ vector

Definition at line 197 of file pcl_base.h.

template<typename PointT >

void pcl::SACSegmentation< PointT >::segment	(	PointIndices &	inliers,
		ModelCoefficients &	model_coefficients
	)		`[virtual, inherited]`

Base method for segmentation of a model in a PointCloud given by <setInputCloud (), setIndices ()>

Parameters:

[in]	inliers	the resultant point indices that support the model found (inliers)
[out]	model_coefficients	the resultant model coefficients

Definition at line 69 of file sac_segmentation.hpp.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setAxis ( const Eigen::Vector3f & ax ) [inline, inherited]

Set the axis along which we need to search for a model perpendicular to.

Parameters:

[in] ax the axis along which we need to search for a model perpendicular to

Definition at line 186 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

void pcl::SACSegmentationFromNormals< PointT, PointNT >::setDistanceFromOrigin ( const double d ) [inline]

Set the distance we expect a plane model to be from the origin.

Parameters:

[in] d distance from the template plane modl to the origin

Definition at line 320 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setDistanceThreshold ( double threshold ) [inline, inherited]

Distance to the model threshold (user given parameter).

Parameters:

[in] threshold the distance threshold to use

Definition at line 123 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setEpsAngle ( double ea ) [inline, inherited]

Set the angle epsilon (delta) threshold.

Parameters:

[in] ea the maximum allowed difference between the model normal and the given axis in radians.

Definition at line 196 of file sac_segmentation.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices ( const IndicesPtr & indices ) [inline, inherited]

Provide a pointer to the vector of indices that represents the input data.

Parameters:

indices a pointer to the vector of indices that represents the input data.

Definition at line 113 of file pcl_base.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices ( const IndicesConstPtr & indices ) [inline, inherited]

Provide a pointer to the vector of indices that represents the input data.

Parameters:

indices a pointer to the vector of indices that represents the input data.

Definition at line 124 of file pcl_base.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices ( const PointIndicesConstPtr & indices ) [inline, inherited]

Provide a pointer to the vector of indices that represents the input data.

Parameters:

indices a pointer to the vector of indices that represents the input data.

Definition at line 135 of file pcl_base.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices	(	size_t	row_start,
		size_t	col_start,
		size_t	nb_rows,
		size_t	nb_cols
	)		`[inline, inherited]`

Set the indices for the points laying within an interest region of the point cloud.

Note:: you shouldn't call this method on unorganized point clouds!

Parameters:

row_start	the offset on rows
col_start	the offset on columns
nb_rows	the number of rows to be considered row_start included
nb_cols	the number of columns to be considered col_start included

Definition at line 151 of file pcl_base.h.

template<typename PointT>

virtual void pcl::PCLBase< PointT >::setInputCloud ( const PointCloudConstPtr & cloud ) [inline, virtual, inherited]

Provide a pointer to the input dataset.

Parameters:

cloud the const boost shared pointer to a PointCloud message

Reimplemented in pcl::PCA< PointT >, and pcl::GeneralizedIterativeClosestPoint< PointT, PointT >.

Definition at line 103 of file pcl_base.h.

template<typename PointT , typename PointNT >

void pcl::SACSegmentationFromNormals< PointT, PointNT >::setInputNormals ( const PointCloudNConstPtr & normals ) [inline]

Provide a pointer to the input dataset that contains the point normals of the XYZ dataset.

Parameters:

[in] normals the const boost shared pointer to a PointCloud message

Definition at line 298 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setMaxIterations ( int max_iterations ) [inline, inherited]

Set the maximum number of iterations before giving up.

Parameters:

[in] max_iterations the maximum number of iterations the sample consensus method will run

Definition at line 133 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setMethodType ( int method ) [inline, inherited]

The type of sample consensus method to use (user given parameter).

Parameters:

[in] method the method type (check method_types.h)

Definition at line 113 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setModelType ( int model ) [inline, inherited]

The type of model to use (user given parameter).

Parameters:

[in] model the model type (check model_types.h)

Definition at line 95 of file sac_segmentation.h.

template<typename PointT , typename PointNT >

void pcl::SACSegmentationFromNormals< PointT, PointNT >::setNormalDistanceWeight ( double distance_weight ) [inline]

Set the relative weight (between 0 and 1) to give to the angular distance (0 to pi/2) between point normals and the plane normal.

Parameters:

[in] distance_weight the distance/angular weight

Definition at line 309 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setOptimizeCoefficients ( bool optimize ) [inline, inherited]

Set to true if a coefficient refinement is required.

Parameters:

[in] optimize true for enabling model coefficient refinement, false otherwise

Definition at line 153 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setProbability ( double probability ) [inline, inherited]

Set the probability of choosing at least one sample free from outliers.

Parameters:

[in] probability the model fitting probability

Definition at line 143 of file sac_segmentation.h.

template<typename PointT>

void pcl::SACSegmentation< PointT >::setRadiusLimits	(	const double &	min_radius,
		const double &	max_radius
	)		`[inline, inherited]`

Set the minimum and maximum allowable radius limits for the model (applicable to models that estimate a radius)

Parameters:

[in]	min_radius	the minimum radius model
[in]	max_radius	the maximum radius model

Definition at line 165 of file sac_segmentation.h.

The documentation for this class was generated from the following files:

/builddir/build/BUILD/PCL-1.5.1-Source/segmentation/include/pcl/segmentation/sac_segmentation.h
/builddir/build/BUILD/PCL-1.5.1-Source/segmentation/include/pcl/segmentation/impl/sac_segmentation.hpp

Public Types

Public Member Functions

Detailed Description

template<typename PointT, typename PointNT> class pcl::SACSegmentationFromNormals< PointT, PointNT >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

template<typename PointT, typename PointNT>
class pcl::SACSegmentationFromNormals< PointT, PointNT >